ML Engineer, Retrieval & Grounded Generation

DEFCON AI, Inc.; Northern (KY); Hybrid; USD 165,000 - 200,000; Full time

Benefits offered by this job

Fully remote

Salary, bonus, and equity package

Health insurance (family)

Unlimited PTO

14 weeks parental leave

Flexible work environment

Job summary

DEFCON AI is seeking an experienced ML Engineer, Retrieval & Grounded Generation to design and implement embeddings, vector storage, and retrieval at scale within a secure government cloud environment.

You will integrate language models to bind generated text to cited records, design prompts and output schemas, and own packaging, versioning, serving, and telemetry to enable measurement without manual reconstruction. This is a fully remote role with occasional travel.

Qualifications

• 5+ years of experience, including a production or near-production retrieval-augmented (RAG) system you built yourself.
• Ability to speak in detail to your retrieval design, which vector store you used and why, how you tested grounding, what citation failures looked like in practice, and how rollback worked
• Strong Python, with hands-on experience in embeddings and vector retrieval at scale
• Clarity on what actually shipped in past work — prototype, proposal, or deployed code — since that distinction matters more here than the title on a resume
• Active US Secret clearance required to start.

Responsibilities

• Implement embeddings, vector storage, and retrieval across a large, provenance-tracked evidence base
• Integrate language models so generated text is bound to cited source records; test for citation failures rather than assuming them away
• Design prompts and output schemas
• Own model packaging, versioning, serving, and rollback
• Instrument telemetry for retrieval and generation quality, attribution, overrides, grounding failures, latency, throughput, and measurement events
• Provide bounded model assistance for difficult narrative extraction where deterministic processing is insufficient, with every output tied to its source passage
• Supply the recorded rule context to every model-assisted step, so each output carries the exact rule versions and ordered context it received
• Maintain a modular in-boundary serving path, self-hosted or managed, alongside the primary managed inference service, so the platform does not depend on one provider’s availability or approval

Skills

RAG systems

Retrieval design

Python proficiency

Shipped code

Security clearance

Vector retrieval

Job description

ML Engineer, Retrieval & Grounded Generation

Remote, USA

ABOUT DEFCON AI

RESILIENCE IN THE FACE OF DISRUPTION. DEFCON AI is an insights company that leverages artificial intelligence, mathematical optimization, data analytics, and software engineering for resilient optimization of complex systems.
In today’s dynamically changing world, DEFCON AI’s technology aligns outcomes with operational goals, better decision making, and empowers customers to anticipate assess, and mitigate the impacts of disruptions.

About the Role

You’ll join the analytics and AI engineering team behind a system that genuinely matters: an AI-assisted platform that pulls together records from dozens of external feeds, resolves them to the right person, surfaces what a human reviewer should look at first, and explains every recommendation in plain, defensible terms — running inside a secure government cloud environment. It’s the kind of problem where the details you get right are the ones that count, which is exactly what makes it worth doing well.

As ML Engineer, Retrieval & Grounded Generation, you’ll build embeddings, vector storage, and retrieval at scale, and integrate language models so that every piece of generated text is bound to cited source records and citation failures are tested for rather than assumed away. You’ll also own prompt and output-schema design; model packaging, versioning, serving, and rollback; and the telemetry hooks that make later measurement possible without manual reconstruction - real infrastructure for a real production system, not a demo.

This is a fully remote role, with occasional travel to DEFCON AI HQ, customer sites, and vendor facilities as required.

Key Responsibilities

• Implement embeddings, vector storage, and retrieval across a large, provenance-tracked evidence base
• Integrate language models so generated text is bound to cited source records; test for citation failures rather than assuming them away
• Design prompts and output schemas
• Own model packaging, versioning, serving, and rollback
• Instrument telemetry for retrieval and generation quality, recommendation/version attribution, overrides, abstentions, grounding failures, latency, throughput, and measurement events defined with Model Test
• Provide bounded model assistance for difficult narrative extraction where deterministic processing is insufficient, with every output tied to its source passage
• Supply the recorded rule context to every model-assisted step, so each output carries the exact rule versions and ordered context it received
• Maintain a modular in-boundary serving path, self-hosted or managed, alongside the primary managed inference service, so the platform does not depend on one provider’s availability or approval

Required Qualifications

• 5+ years of experience, including a production or near-production retrieval-augmented (RAG) system you built yourself
• Ability to speak in detail to your retrieval design, which vector store you used and why, how you tested grounding, what citation failures looked like in practice, and how rollback worked
• Strong Python, with hands‑on experience in embeddings and vector retrieval at scale
• Clarity on what actually shipped in past work — prototype, proposal, or deployed code — since that distinction matters more here than the title on a resume
• Active US Secret clearance required to start.

Preferred Qualifications

• Experience deploying models into restricted or air‑gapped environments
• Self-hosted or open‑weight model operation
• Fine‑tuning, adapters, or custom embeddings
• Federal DevSecOps, RMF, ATO, or DoW cloud environment experience

What Success Looks Like

• Generated explanations that assert no more than the sources support, with the citation path intact and citation failures tested rather than assumed away
• A retrieval system that performs at scale on a large, provenance-tracked evidence base
• Model rollback that works when it’s needed, with telemetry complete enough that measurement does not require manual reconstruction

What We Offer

• A fully remote, results‑based environment
• Competitive salary, bonus, and equity package
• 100% employer paid, comprehensive health insurance including medical, dental, and vision for you and your family
• Unlimited PTO, with your manager's approval
• Flexible work environment where you manage your work day
• 14 weeks of fully‑paid parental leave

Salary Range: $165,000–$200,000. This represents the typical salary range for this position based on experience, skills, and other factors.

We’re an Equal Opportunity Employer: You’ll receive consideration for employment without regard to race, sex, color, religion, sexual orientation, gender identity, national origin, protected veteran status, or on the basis of disability.

Voluntary Self-Identification

For government reporting purposes, we ask candidates to respond to the below self-identification survey. Completion of the form is entirely voluntary. Whatever your decision, it will not be considered in the hiring process or thereafter. Any information that you do provide will be recorded and maintained in a confidential file.

As set forth in DEFCON AI’s Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.

If you believe you belong to any of the categories of protected veterans listed below, please indicate by making the appropriate selection. As a government contractor subject to the Vietnam Era Veterans Readjustment Assistance Act (VEVRAA), we request this information in order to measure the effectiveness of the outreach and positive recruitment efforts we undertake pursuant to VEVRAA. Classification of protected categories is as follows:

A "disabled veteran" is one of the following: a veteran of the U.S. military, ground, naval or air service who is entitled to compensation (or who but for the receipt of military retired pay would be entitled to compensation) under laws administered by the Secretary of Veterans Affairs; or a person who was discharged or released from active duty because of a service-connected disability.

A "recently separated veteran" means any veteran during the three-year period beginning on the date of such veteran's discharge or release from active duty in the U.S. military, ground, naval, or air service.

An "active duty wartime or campaign badge veteran" means a veteran who served on active duty in the U.S. military, ground, naval or air service during a war, or in a campaign or expedition for which a campaign badge has been authorized under the laws administered by the Department of Defense.

An "Armed forces service medal veteran" means a veteran who, while serving on active duty in the U.S. military, ground, naval or air service, participated in a United States military operation for which an Armed Forces service medal was awarded pursuant to Executive Order 12985.